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Determining disaster severity through social media analysis: testing the methodology with South East Queensland Flood tweets

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dc.contributor.author Goonetilleke, Ashantha
dc.date.accessioned 2026-03-05T04:05:03Z
dc.date.available 2026-03-05T04:05:03Z
dc.date.issued 2020-01
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S2212420919307940
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20783
dc.description.abstract Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Civil engineering en_US
dc.subject Social media en_US
dc.subject Data analytics en_US
dc.subject Big data en_US
dc.subject Crowdsourcing en_US
dc.subject Volunteered geographic information en_US
dc.subject South East Queensland Floods en_US
dc.title Determining disaster severity through social media analysis: testing the methodology with South East Queensland Flood tweets en_US
dc.type Article en_US


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